Soil Organic Carbon (SOC) is important for soil fertility and can be quickly retrieved by Visible Near-Infrared (VNIR) Spectroscopy, which provides a basis for regional monitoring and quantitative remote sensing. For the traditional Continuum Removal (CR) method, only the upside absorption characteristics of the reflection spectrum envelope is considered in multiple regression, which results in the absence of CR downside or predictive spectral background information, thus the variables usually do not reflect the emission characteristics of all band . In this paper, a new method named BCR-OPLS which combines Bi-Continuum Removal (BCR) and Orthogonal Partial Least-Squares (OPLS) is proposed for SOC content retrieval, conducting a test upon 245 Chinese soil samples containing VNIR (350~2 500 nm) diffuse reflectance spectra downloaded from ICRAF-ISRI Database. With BCR-OPLS method, both the upside and downside continuum removal are included in analyzing the characteristics of the spectra. After building the comprehensive and classification model for soils of different types mixed and alone, an SOC index applicable to certain type of soil is derived. The role of power function and logarithmic function playing in skewness correction for the SOC reference values' statistical distribution is discussed. As a result, by introducing bilateral-continuum information, the SOC retrieval ability of the BCR-OPLS model is significantly improved (Coefficients of determination R2=0.9 and Root mean square error Estimated RMSEE=0.26%) compared with the initial R-PLSR model (R2=0.69, RMSEE=0.45%), and the SOC retrieval accuracy of a certain type is further improved. For example, when predicting SOC of the Orthic Ferralsols (using 400, 590 and 920 nm), R2 and RMSEE improved to be 0.94 and 0.21% respectively. In summary, BCR-OPLS enhances the robustness of spectral feature diagnostics by improving the accuracy of both comprehensive and classified SOC inversion based on full-spectrum, and derives a simple SOC prediction index composed of several wavelength variables for a certain type of soil through the translatability of relationships among BCR and SOC content revealed in loading scatter plot of OPLS, which are selected according to the loadings' trend and Variable Importance in Projection. Finally, BCR-OPLS strengthens the connection between experienced physical absorption analysis and obscure statistical multiple regression method.